The Geochemical and mineralogical characterization of Tajimi iron ore in Kogi State and determination of its flotability nature

This research focuses on the geochemical and mineralogical characterization of Tajimi iron ore, located in Kogi State, Nigeria, with the aim of evaluating its industrial potential through froth flotation. A comprehensive analysis of the ore was conducted using techniques such as X-ray Fluorescence (XRF), X-ray Diffraction (XRD), Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM/EDS), and petrographic analysis, followed by a beneficiation via froth flotation method. The chemical composition of the crude ore was determined to be 62.166% Fe2O3 and 18.568% SiO2, with other trace elements also identified. Mineralogical analysis revealed the presence of goethite and cristobalite as the dominant minerals, with significant interlocking within the ore matrix, which facilitates the comminution process. Froth flotation was employed to enhance the iron concentration, resulting in a froth concentrate with 68.260% Fe2O3 and a depressed product with 68.006% Fe2O3. The recovery rate of iron oxide in the concentrate was 32.941%, with an enrichment ratio of 1.098 and a concentration ratio of 3.333, indicating a successful beneficiation process. The findings suggest that Tajimi iron ore has significant industrial potential, though further refinement is needed to reduce silica and other impurities

Application of Box-Behnken Design for the Optimization of the production of pyrolytic bio-oil from Udara seed in a fixed bed reactor through pyrolysis process

This work optimized the production of bio-oil from udara seeds using pyrolysis. Response surface methodology (RSM) was utilized to analyze the concurrent impact of temperature, particle size diameter, and inert gas flow rate on the percentage yield of bio-oil during the pyrolysis of udara seed. A three-variable, five-level Box-Behnken design (BBD) consisting of 17 experimental runs was employed to formulate a quadratic model for optimizing pyrolysis conditions. The optimum pyrolysis parameters for achieving the highest bio-oil production were a temperature of 422.9 °C, a particle size diameter of 2.5 mm, and an inert gas flow rate of 1.42 L/min. Under these conditions, the bio-oil yield was determined to be 59.73%. The model validation revealed no substantial discrepancy between anticipated and observed values. GC-MC analysis indicates that the predominant monounsaturated fatty acid made up 45.55% of the total fatty acid content, which depicts that the oil belongs to the linoleic acid group. The FTIR analysis reveals that the alkene group contributes to increased reactivity and combustion efficiency, boosts the octane number of the bio-oil, and decreases the boiling point of the oil. FTIR and GC-MS analysis findings confirm that the bio-oil was within ASTM specifications

Influence of ethanol on the spectral properties of natural dyes from microbes: implications for dye-sensitized solar cell performance

The increasing need for renewable energy sources that are both sustainable and less costly has spurred growing research into natural dyes as eco-friendly sensitizers for dye-sensitized solar cells (DSSCs). This study entails the extraction and characterization of natural pigments derived from Chlamydomonas starii and Coelastrella sp. focusing on solvent selection and thermal treatment. Ethanol proved much better than ethyl acetate and had the most remarkable effects on retaining functional groups, especially hydroxyl (O-H) and carbonyl (C=O), which would be important in terms of dye adsorption for improved photon absorption and electron injection. Spectroscopic analyses indicated that the ethanol-extracted dyes, especially non-heated and moderately heated ones, would enable a broad absorption of light within the visible spectrum. These findings hence have demonstrated that natural dyes extracted with ethanol were better than those extracted with ethyl acetate, which makes ethanol more efficient, scalable, and greener solvent for extraction of natural dyes compared to ethyl acetate. Future research is to focus on mixed solvent systems and stabilization techniques to improve dye performance even further. This will therefore be the greatest scion towards high-efficiency DSSCs while further supporting the global transition efforts toward sustainable energy technologies

Speed control enhancement of a brushless direct current motor using transit search optimizer-based proportional integral derivatives controller

Brushless Direct Current (BLDC) motors are capable of achieving accurate speed control tailored to the requirements of various applications, making them suitable for devices that need high-precision motion management, such as robots and medical devices. However, most motors perform poorly as a result of commutation problems where the switching of current is controlled by an electronics speed controller (ESC) thereby causing variation in speed. Linear Quadratic Regulator (LQR) and Proportional Integral Derivatives (PID) controllers have been used to control the speed but with little expected result due to maximum overshoot which leads to system instability. This research aimed to control the Speed of a Brushless DC Motor using a Transit Search Optimizer (TSO) based PID controller. The dynamic mathematical model for the DC motor and PID controller was formulated. Then, the TSO-PID model for DC speed motor control was developed. Simulation of the developed model was done using MATLAB R2021a. The performance evaluation and comparison of the developed model with Simulated Annealing (SA) and Nelder Mead-PID (SA-NM-PID) using rise time, settling time, and percentage overshoot as metrics. The outcome of the results showed that the developed TSO-PID outperformed other conventional methods in terms of rise time, settling time, and percentage overshoot. The application of this work can be considered for domestic and industrial control of drives

Mechanical properties and microstructural analysis of reinforcement steel bars in Osun State construction industry

Steel bars are crucial components in structural engineering. The frequent incidents of building collapse in Nigeria highlight the importance of carefully analysing the characteristics of reinforcement steel bars available in the local market. A study was conducted in Osun State to evaluate the compliance of locally available steel bars with essential standards, addressing concerns related to building integrity. The study examined the mechanical properties of reinforcement steel rods with diameters of 7, 9, 12, and 14 mm, procured from four prominent dealers in the Osun State market. Standard procedures were employed to determine hardness values, yield strength, and ultimate tensile strength, utilizing an Instron Satec Series 600DX universal testing machine. Additionally, Scanning Electron Microscopy (SEM) was employed to investigate microstructural properties at the metallurgy laboratory of SARD and the Department of Materials Science and Engineering, Obafemi Awolowo University, Ile-Ife, Osun State. The findings showed that certain steel bars exceeded the hardness values, yield strengths, and ultimate tensile strengths set by BS4449, ISO, NIS, and ASTM A706. However, the studied steel bar samples showed commendable ductility. A strong correlation was established between microstructure and mechanical properties. It is noteworthy that the samples contained obvious levels of impurities. In conclusion, while the samples demonstrated satisfactory ductility, it is important to take into consideration the presence of impurities

Investigation of process parameters for producing bio-oil from luffa cylindrical fiber in a fixed bed reactor using pyrolysis process

This study was performed to assess the impact of pyrolysis parameters on the yield of pyrolytic bio-oil during the thermal degradation of luffa cylindrical fiber in a fixed-bed reactor. The study revealed that the optimal bio-oil output of 29 wt% was attained at a reactor temperature of 600 °C, a biomass particle size of 4 mm, and a nitrogen gas flow rate of 1.5 L/min. The Gas Chromatography-Mass Spectrometry (GC-MS) analysis of the bio-oil revealed the presence of phenols, alcohols, carboxylic acids, ketones, alkenes, alkanes, aldehydes, and aromatics, indicating that the pyrolysis of luffa cylindrical fiber could be a viable approach for producing renewable fuels and chemicals while mitigating environmental pollution concerns

Evaluation of biogas yield from co-digestion of varying particle sizes of corncob with poultry manure and process parameters optimization study

The need to improve renewable energy generation, advance sustainable waste management techniques, and uncover beneficial agriculture methods, necessitated anaerobic co-digestion of grounded corncob (GC) biologically pretreated with cattle rumen (inoculum) and poultry poos (PP) for biogas generation, as discussed in this study, Corncob biomass (CB) and PP (substrates) were obtained at the teaching and research laboratory in LAUTECH Ogbomoso. CB was pretreated using mechanical grinding and sieving methods and then divided into two portions labelled A and B, using sieve sets of 0.30mm and 0.45mm. The ratio of the combination of substrates: GC: PP: inoculum is 1:0.5:0.5. Standard procedures were used to assess the physicochemical parameters of the substrates and digestates. Central Composite Design (CCD) was used to batch the experimental design of pretreated samples A and B with PP and inoculum to produce biogas which was analysed for methane content using a gas chromatograph mass spectrometer. Response Surface Methodology (RSM) was used to optimize data generated for temperature, pH, retention time, total solids, and volatile solids (VS) using the 'Design-Expert Application' version 11. The biogas yields for experiments A and B were 1.368 L/kg VS and 1.221 L/kg VS, while the Methane compositions were 60.44% and 57.58%, respectively. The optimized data for A and B were; temperature (40 o C, 40 o C); pH (8.0, 6.0); retention time (30, 30 days); total solids (12, 4 g/kg); and volatile solids (12, 12 g/kg) respectively. The model's coefficient of determination (R2) was high (0.9267) for A, indicating strong modelling and prediction accuracy, thus, recommending the usage of corncob for bioenergy generation

Techno-Economic and Environmental Analysis of an Off-grid Hybrid Renewable Energy System for Rural Electrification

Rural electrification is key to socio-economic development in developing countries like Nigeria. However, extending the national grid to remote rural areas is expensive and time consuming, and the traditional power supply method also involves utilization of a standalone renewable energy source such as Solar which have their associated drawbacks due to their unreliability in nature. Hence, this research carried out a techno-economic and environmental analysis of an off-grid hybrid renewable energy system for rural electrification using Kepler Optimization Algorithm (KOA). Feasibility study on electricity and hourly load demand assessment of Alayin village was conducted and village load profile was estimated. Mathematical modeling of each hybrid RES was formulated. A KOA technique was employed to carry out optimal sizing and check the cost efficiency of BG/ PHES/ Battery, PV/PHES/Battery, and the hybrid RES (PV/BG/PHES/Battery). Simulation of the model was done using MATLAB R2021a. The value obtained was validated with Gravitational Search Algorithm (GSA) for performance evaluation using Levelized Cost of Energy (LCOE), Loss of Power Supply Probability (LPSP) and CO2 reduction in manure management as performance metrics. The results of the analysis showed an appreciable reduction on the LCOE and CO2 with high reliability using KOA-hybrid RES compared with GSA-hybrid RES

Influence of penetration angles on global-thermo-hydraulic-performance of shell and tube heat exchangers with multi-cross sectional tube configurations

Shell-and-Tube Heat Exchanger (STHE), a vital component for efficient energy management when used with Straight-Tube Geometries (STG) is associated with low Global-Thermo-Hydraulic-Performance (GTHP). These contribute to the high energy demand of processing plants. The recently developed STHEs with modified tube configurations have not adequately addressed these limitations and necessitated a continuous search for tubes with improved performance. Multi-cross sectional tube geometrical (MSTG) configurations are known to improve GTHP along flow lines. This process has not been thoroughly investigated. Therefore, this study was designed to investigate the influence of penetration angles (PAs) on the STHEs’ performances using MSTG configurations. ; The numerical analysis was evaluated in terms of GTHP indices on STHE with Convergent-Divergent-Tube-Geometry (CDTG) and Divergent-Convergent-Tube-Geometry (DCTG) configurations of varying penetration angles (PAs), 5,10,15,…,90°. Numerical GTHP for STHE with STG was 1.0, while that obtained for STHE with CDTG configurations for all PAs fall between 1.50 to 1.625 with the highest at  PA indicating a 50% minimum improvement in GTHP over STHE-STG. For DCTG, GTHP were between 1.43 and 1.585 for all PAs with the highest at  PA indicating a 43% minimum improvement in GTHP over STHE-STG. Replacing STHE-STG with STHE-MSTG can improved their GTHPs in processing plant

Soft computing based load forecasting using artificial neural networks: a case study of Lagos, Nigeria

This study introduces a soft computing approach using Artificial Neural Networks (ANN) for load forecasting, specifically focusing on predicting the minimum and maximum load power. The goal is to efficiently allocate the expected power to suitable load centers. The analysis utilizes a 3-year historical dataset of load consumption in Lagos, a city in Western Nigeria. A Multi-layered Perceptron (MLP) network is employed to generate short-term load forecasts for the area. The inputs for the network include monthly data, while the output parameters are load data obtained from the energy company, which are used to predict power needs in the geographical area. The ANN training employs supervised learning and the back-propagation algorithm, implemented through MATLAB & SIMULINK. The input and target data are preprocessed and normalized within the range of -1 and 1. The network is continuously trained until desirable regression values and a disparity graph are achieved. The study demonstrates significant success with regression values of 0.96, 0.97, and 0.97 obtained over three consecutive years (2021/2022, 2022/2023 and 2023/2024) which indicate that the model accurately predict the load of year 2024. The developed model holds promise for independent power companies in Nigeria to enhance load allocation planning and forecast expected revenue